Neural Computing and Applications

, Volume 23, Issue 5, pp 1421–1426 | Cite as

Gene-expression programming to predict friction factor for Southern Italian rivers

  • H. Md. Azamathulla
Original Article


This briefing article presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to predict friction factor for Southern Italian rivers. Published data were compiled for the friction for 43 gravel-bed rivers of Calabria. The proposed GEP approach produces satisfactory results (R 2 = 0.958 and RMSE = 0.079) compared with existing predictors.


Rivers Friction factor GEP Streams Gravel-bed 


  1. 1.
    Alavi AH, Gandomi AH (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28(3):242–274Google Scholar
  2. 2.
    Alavi AH, Ameri M, Gandomi AH, Mirzahosseini MR (2011) Formulation of flow number of asphalt mixes using a hybrid computational method. Constr Build Mater 25(3):1338–1355CrossRefGoogle Scholar
  3. 3.
    Azamathulla HMd, Ghani AA, Zakaria NA, Guven A (2010) Genetic programming to predict bridge pier scour. ASCE J Hydraul Eng 136(3):165–169CrossRefGoogle Scholar
  4. 4.
    Bathurst JC, Li R, Simons DB (1981) Resistance equation for large-scale roughness. J Hydraul Div ASCE 107(HY12):1593–1613Google Scholar
  5. 5.
    Bray DI (1979) Estimating average velocity in gravel-bed rivers. J Hydraul Div ASCE 105(HY12):1103–1122Google Scholar
  6. 6.
    Colosimo C, Vito AC, Veltri M (1988) Friction factor evaluation in gravel-bed rivers. J Hydraul Eng 114(8):861–876CrossRefGoogle Scholar
  7. 7.
    Ferreira C (2001) Gene expression programming in problem solving. 6th Online world conference on soft computing in industrial applications (invited tutorial)Google Scholar
  8. 8.
    Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129zbMATHGoogle Scholar
  9. 9.
    Gandomi AH, Alavi AH, Mirzahosseini MR, Moqhadas Nejad F (2011) Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J Mater Civ Eng ASCE 23(3):248–263CrossRefGoogle Scholar
  10. 10.
    Gandomi AH, Babanajad SK, Alavi AH, Farnam Y (2012) A novel approach to strength modeling of concrete under triaxial compression. J Mater Civ Eng ASCE. doi: 10.1061/(ASCE)MT.1943-5533.0000494
  11. 11.
    Gandomi AH, Tabatabaie SM, Moradian MH, Radfar A, Alavi AH (2011) A new prediction model for load capacity of castellated steel beams. J Constr Steel Res 67(7):1096–1105CrossRefGoogle Scholar
  12. 12.
    GEPSOFT (2006) GeneXproTools. Version 4.0,
  13. 13.
    Giustolisi O (2004) Using genetic programming to determine Chèzy resistance coefficient in corrugated channels. J Hydroinform 6(3):157–173Google Scholar
  14. 14.
    Graf WH, Cao HH, Suszka L (1983) Hydraulics of steep mobile-bed channels. In: Proceedings of 20th congress of the international association for hydraulic research, Moscow, USSRGoogle Scholar
  15. 15.
    Griffiths GA (1981) F low resistance in coarse gravel-bed rivers. J Hydraul Div ASCE 107(HY7):899–916Google Scholar
  16. 16.
    Guven A, Gunal M (2008) Genetic programming approach for prediction of local scour downstream of hydraulic structures. J Irrig Drain Eng 134(2):241–249CrossRefGoogle Scholar
  17. 17.
    Guven A, Gunal M (2008) Prediction of scour downstream of grade-control structures using neural networks. J Hydraul Eng 134(11):1656–1660CrossRefGoogle Scholar
  18. 18.
    Guven A, Aytek A (2009) A new approach for stage-discharge relationship: gene-expression programming. ASCE J Hydrol Eng 14(8):812–820CrossRefGoogle Scholar
  19. 19.
    Hey RD (1979) Flow resistance in gravel-bed rivers. J Hydraul Div ASCE 105(HY4):365–379Google Scholar
  20. 20.
    Jarrett RD (1994) Historic-flood evaluation and research needs in mountainous areas. In: Cotroneo GV, Rumer RR (eds) Hydraulic engineering—proceedings of the symposium sponsored by the American Society of Civil Engineers, Buffalo, NY, 1–5 Aug 1994. American Society of Civil Engineers, New York, pp 875–879Google Scholar
  21. 21.
    Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. A Bradford book. MIT Press, CambridgeGoogle Scholar
  22. 22.
    Kumar B (2011) Flow resistance in alluvial channel. Water Resour 38(6):745–754Google Scholar
  23. 23.
    Kumar B, Rao AR (2010) Metamodeling approach to predict friction factor of alluvial channel. Comput Electron Agric 70(1):144–150CrossRefGoogle Scholar
  24. 24.
    Kumar B, Bhatla A (2010) Genetic algorithm optimized neural network prediction of the friction factor in a mobile bed channel. J Intell Syst 19(4):315–335Google Scholar
  25. 25.
    Limerinos JT (1970) Determination of the Manning coefficient from measured bed roughness in natural channels. Water supply paper 1898-B, USGS, Washington, DCGoogle Scholar
  26. 26.
    Marchi E (1961) II moto uniforme delle correnti liquid nei condotti chiusi e aperti. L’ Energia Elettrica, Milano, Italy, No. 4–5, 289–301 (Italian)Google Scholar
  27. 27.
    Motamedi A, Afzalimehr H, Singh VP (2010) Estimation of friction factor in open channels. J Hydrol Eng 15(3):249–254CrossRefGoogle Scholar
  28. 28.
    Peterson DF, Mohanty PK (1960) Flume studies of flow in steep rough channels. J Hydraul Div ASCE 86(HY9):55–76Google Scholar
  29. 29.
    Rouse H (1965) Critical analysis of open-channel resistance. J Hydraul Div ASCE 91(HY4):1–25Google Scholar
  30. 30.
    Teodorescu L, Sherwood D (2008) High energy physics event selection with gene expression programming. Comput Phys Commun 178(6):409–419Google Scholar
  31. 31.
    Yalin MS (1972) Mechanics of sediment transport, 1st edn. Pergamon Press, New YorkGoogle Scholar
  32. 32.
    Yarahamadi MB, Fathi-Moghadam M, Bajestan MS (2010) Effects of land slope and flow depth on retarding flow in gravel-bed lands. World Appl Sci J 8(8):943–947Google Scholar
  33. 33.
    Yen BC (2002) Open channel flow resistance. J Hydraul Eng 128(1):20–39CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaNibong TebalMalaysia

Personalised recommendations